inf5402 - Trustworthy Machine Learning (Course overview)

Department of Health Services Research

6 KP

Module components

Semester courses Summer semester 2025

Examination

Lecture

2.01.5402 - Trustworthy Machine Learning
- Prof. Dr. Nils Strodthoff
- Zahra Mansour
Wednesday: 12:00 - 14:00, weekly (from 09/04/25)
Thursday: 08:00 - 10:00, weekly (from 10/04/25)

Machine learning algorithms find its way into an increasing number of (safety-critical) application domains but their quality is rarely assessed in a systematic way. The focus of this module are quality criteria for machine learning algorithms, in particular of deep neural networks, ranging from performance evaluation over explainability/interpretability (XAI), robustness (adversarial robustness, robustness against input perturbations), uncertainty quantification, distribution shift, domain adaptation, fairness/bias to privacy. The methods are introduced theoretically in the lecture and implemented/applied practically in the exercises. Prerequisites are basic theoretical knowledge in machine learning, programming skills in Python and ideally practical knowledge in training neural networks.

Exercises

2.01.5402 - Trustworthy Machine Learning
- Prof. Dr. Nils Strodthoff
- Zahra Mansour
Wednesday: 12:00 - 14:00, weekly (from 09/04/25)
Thursday: 08:00 - 10:00, weekly (from 10/04/25)

Machine learning algorithms find its way into an increasing number of (safety-critical) application domains but their quality is rarely assessed in a systematic way. The focus of this module are quality criteria for machine learning algorithms, in particular of deep neural networks, ranging from performance evaluation over explainability/interpretability (XAI), robustness (adversarial robustness, robustness against input perturbations), uncertainty quantification, distribution shift, domain adaptation, fairness/bias to privacy. The methods are introduced theoretically in the lecture and implemented/applied practically in the exercises. Prerequisites are basic theoretical knowledge in machine learning, programming skills in Python and ideally practical knowledge in training neural networks.

Notes on the module

Prerequisites	Content requirements are basic theoretical knowledge in machine learning, practical programming knowledge in Python basic knowledge in deep neural network training.
Prüfungszeiten	At the end of the lecture term
Module examination	Written or oral Exam Active participation: Handing in exercises
Skills to be acquired in this module	Professional competence The students have an overview of the various aspects that determine the quality of machine learning algorithms. are familiar with methods to measure different quality aspects and, if necessary, methods to enhance them, and they can implement and apply these methods. Methodological competence The students independently develop theoretical and practical concepts with the help of in-person events, provided materials, and specialized literature. Social competence The students can present solution approaches for problems in this area to the plenary and defend them in discussions. Self-competence The students are able to assess their own subject-specific and methodological competence. They take responsibility for their competence development and learning progress and reflect on these independently. In addition, they independently work on learning content and can critically reflect on the content. General compentence goals ++ knowledge of data science/ML methods and its foundations ++ Ability to analyze problems, compare and select solution methods + formalizing problems mathematically, developing and implementing solutions, interpret their results + knowledge of ethical, legal, security-related limitations + data presentation & discussion + Scientific literature (reading & writing) + scientific communication skills (in particular with people outside the field of study)